Overview

Dataset statistics

Number of variables18
Number of observations213
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory53.8 KiB
Average record size in memory258.9 B

Variable types

Categorical2
Numeric16

Warnings

Country has a high cardinality: 213 distinct values High cardinality
Total Cases is highly correlated with New Cases and 6 other fieldsHigh correlation
New Cases is highly correlated with Total Cases and 6 other fieldsHigh correlation
Total Deaths is highly correlated with Total Cases and 6 other fieldsHigh correlation
New Deaths is highly correlated with Total Cases and 6 other fieldsHigh correlation
Total Recovered is highly correlated with Total Cases and 6 other fieldsHigh correlation
New Recovered is highly correlated with Total Cases and 6 other fieldsHigh correlation
Active Cases is highly correlated with Total Cases and 6 other fieldsHigh correlation
Serious/Critical is highly correlated with Total Cases and 6 other fieldsHigh correlation
Country is uniformly distributed Uniform
Country has unique values Unique

Reproduction

Analysis started2021-04-27 00:27:57.992552
Analysis finished2021-04-27 00:28:27.014834
Duration29.02 seconds
Software versionpandas-profiling v2.11.0
Download configurationconfig.yaml

Variables

Country
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct213
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size13.8 KiB
Honduras
 
1
Bolivia
 
1
Réunion
 
1
Monaco
 
1
Total:
 
1
Other values (208)
208 

Length

Max length22
Median length7
Mean length8.399061033
Min length2

Characters and Unicode

Total characters1789
Distinct characters57
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique213 ?
Unique (%)100.0%

Sample

1st rowWorld
2nd rowUSA
3rd rowIndia
4th rowBrazil
5th rowFrance
ValueCountFrequency (%)
Honduras1
 
0.5%
Bolivia1
 
0.5%
Réunion1
 
0.5%
Monaco1
 
0.5%
Total:1
 
0.5%
CAR1
 
0.5%
French Guiana1
 
0.5%
Turkey1
 
0.5%
Suriname1
 
0.5%
Togo1
 
0.5%
Other values (203)203
95.3%
2021-04-27T05:58:27.239255image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
and6
 
2.2%
islands6
 
2.2%
guinea3
 
1.1%
saint3
 
1.1%
new3
 
1.1%
sudan2
 
0.7%
french2
 
0.7%
south2
 
0.7%
netherlands2
 
0.7%
st2
 
0.7%
Other values (237)237
88.4%

Most occurring characters

ValueCountFrequency (%)
a279
15.6%
n149
 
8.3%
i148
 
8.3%
e119
 
6.7%
r102
 
5.7%
o95
 
5.3%
l69
 
3.9%
u66
 
3.7%
t65
 
3.6%
s61
 
3.4%
Other values (47)636
35.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1455
81.3%
Uppercase Letter272
 
15.2%
Space Separator56
 
3.1%
Other Punctuation4
 
0.2%
Dash Punctuation2
 
0.1%

Most frequent character per category

ValueCountFrequency (%)
a279
19.2%
n149
10.2%
i148
10.2%
e119
 
8.2%
r102
 
7.0%
o95
 
6.5%
l69
 
4.7%
u66
 
4.5%
t65
 
4.5%
s61
 
4.2%
Other values (18)302
20.8%
ValueCountFrequency (%)
S33
12.1%
M25
 
9.2%
C24
 
8.8%
B22
 
8.1%
A18
 
6.6%
G17
 
6.2%
I16
 
5.9%
L13
 
4.8%
T12
 
4.4%
P12
 
4.4%
Other values (15)80
29.4%
ValueCountFrequency (%)
.3
75.0%
:1
 
25.0%
ValueCountFrequency (%)
56
100.0%
ValueCountFrequency (%)
-2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1727
96.5%
Common62
 
3.5%

Most frequent character per script

ValueCountFrequency (%)
a279
16.2%
n149
 
8.6%
i148
 
8.6%
e119
 
6.9%
r102
 
5.9%
o95
 
5.5%
l69
 
4.0%
u66
 
3.8%
t65
 
3.8%
s61
 
3.5%
Other values (43)574
33.2%
ValueCountFrequency (%)
56
90.3%
.3
 
4.8%
-2
 
3.2%
:1
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII1787
99.9%
None2
 
0.1%

Most frequent character per block

ValueCountFrequency (%)
a279
15.6%
n149
 
8.3%
i148
 
8.3%
e119
 
6.7%
r102
 
5.7%
o95
 
5.3%
l69
 
3.9%
u66
 
3.7%
t65
 
3.6%
s61
 
3.4%
Other values (45)634
35.5%
ValueCountFrequency (%)
é1
50.0%
ç1
50.0%

Total Cases
Real number (ℝ≥0)

HIGH CORRELATION

Distinct212
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2081168.174
Minimum3
Maximum147797848
Zeros0
Zeros (%)0.0%
Memory size1.8 KiB
2021-04-27T05:58:27.362548image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile167.4
Q16224
median47776
Q3306400
95-th percentile3371708.2
Maximum147797848
Range147797845
Interquartile range (IQR)300176

Descriptive statistics

Standard deviation14496231.89
Coefficient of variation (CV)6.965430318
Kurtosis96.14770919
Mean2081168.174
Median Absolute Deviation (MAD)47553
Skewness9.724600902
Sum443288821
Variance2.101407391 × 1014
MonotocityNot monotonic
2021-04-27T05:58:27.487252image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1477978482
 
0.9%
10221
 
0.5%
42831
 
0.5%
416551
 
0.5%
1752861
 
0.5%
68351
 
0.5%
3130061
 
0.5%
1947331
 
0.5%
101
 
0.5%
3975001
 
0.5%
Other values (202)202
94.8%
ValueCountFrequency (%)
31
0.5%
101
0.5%
201
0.5%
251
0.5%
271
0.5%
491
0.5%
631
0.5%
721
0.5%
911
0.5%
1241
0.5%
ValueCountFrequency (%)
1477978482
0.9%
328261821
0.5%
173063001
0.5%
143407871
0.5%
54980441
0.5%
47625691
0.5%
46299691
0.5%
44048821
0.5%
39626701
0.5%
34819691
0.5%

New Cases
Real number (ℝ)

HIGH CORRELATION

Distinct141
Distinct (%)66.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10484.67136
Minimum-1
Maximum744434
Zeros0
Zeros (%)0.0%
Memory size1.8 KiB
2021-04-27T05:58:27.620908image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median105
Q31254
95-th percentile12470
Maximum744434
Range744435
Interquartile range (IQR)1255

Descriptive statistics

Standard deviation75784.20227
Coefficient of variation (CV)7.228095155
Kurtosis84.39469934
Mean10484.67136
Median Absolute Deviation (MAD)106
Skewness9.09489954
Sum2233235
Variance5743245313
MonotocityNot monotonic
2021-04-27T05:58:27.743531image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-154
 
25.4%
324
 
1.9%
183
 
1.4%
33
 
1.4%
43
 
1.4%
7444342
 
0.9%
82
 
0.9%
672
 
0.9%
782
 
0.9%
52
 
0.9%
Other values (131)136
63.8%
ValueCountFrequency (%)
-154
25.4%
11
 
0.5%
22
 
0.9%
33
 
1.4%
43
 
1.4%
52
 
0.9%
61
 
0.5%
82
 
0.9%
101
 
0.5%
141
 
0.5%
ValueCountFrequency (%)
7444342
0.9%
3545311
0.5%
420971
0.5%
385531
0.5%
325721
0.5%
244651
0.5%
191651
0.5%
171901
0.5%
150121
0.5%
131541
0.5%

Total Deaths
Real number (ℝ)

HIGH CORRELATION

Distinct186
Distinct (%)87.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43957.57277
Minimum-1
Maximum3122572
Zeros0
Zeros (%)0.0%
Memory size1.8 KiB
2021-04-27T05:58:27.874232image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile1
Q189
median736
Q36209
95-th percentile79508.2
Maximum3122572
Range3122573
Interquartile range (IQR)6120

Descriptive statistics

Standard deviation305404.4076
Coefficient of variation (CV)6.947708628
Kurtosis97.22329635
Mean43957.57277
Median Absolute Deviation (MAD)735
Skewness9.788105531
Sum9362963
Variance9.327185218 × 1010
MonotocityNot monotonic
2021-04-27T05:58:27.993399image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-110
 
4.7%
16
 
2.8%
262
 
0.9%
292
 
0.9%
1412
 
0.9%
982
 
0.9%
102
 
0.9%
352
 
0.9%
852
 
0.9%
22
 
0.9%
Other values (176)181
85.0%
ValueCountFrequency (%)
-110
4.7%
16
2.8%
22
 
0.9%
32
 
0.9%
51
 
0.5%
61
 
0.5%
102
 
0.9%
122
 
0.9%
151
 
0.5%
161
 
0.5%
ValueCountFrequency (%)
31225722
0.9%
5861571
0.5%
3909251
0.5%
2148531
0.5%
1951161
0.5%
1274281
0.5%
1192381
0.5%
1082321
0.5%
1028581
0.5%
822371
0.5%

New Deaths
Real number (ℝ)

HIGH CORRELATION

Distinct63
Distinct (%)29.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean140.4647887
Minimum-1
Maximum10005
Zeros0
Zeros (%)0.0%
Memory size1.8 KiB
2021-04-27T05:58:28.125843image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median1
Q318
95-th percentile247.8
Maximum10005
Range10006
Interquartile range (IQR)19

Descriptive statistics

Standard deviation987.8870557
Coefficient of variation (CV)7.032987161
Kurtosis93.71018907
Mean140.4647887
Median Absolute Deviation (MAD)2
Skewness9.581743629
Sum29919
Variance975920.8348
MonotocityNot monotonic
2021-04-27T05:58:28.253539image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-196
45.1%
113
 
6.1%
48
 
3.8%
28
 
3.8%
56
 
2.8%
105
 
2.3%
114
 
1.9%
64
 
1.9%
34
 
1.9%
193
 
1.4%
Other values (53)62
29.1%
ValueCountFrequency (%)
-196
45.1%
113
 
6.1%
28
 
3.8%
34
 
1.9%
48
 
3.8%
56
 
2.8%
64
 
1.9%
71
 
0.5%
92
 
0.9%
105
 
2.3%
ValueCountFrequency (%)
100052
0.9%
28061
0.5%
13161
0.5%
4651
0.5%
4541
0.5%
3491
0.5%
3471
0.5%
3321
0.5%
2841
0.5%
2731
0.5%

Total Recovered
Real number (ℝ≥0)

HIGH CORRELATION

Distinct211
Distinct (%)99.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1770833.634
Minimum2
Maximum125762412
Zeros0
Zeros (%)0.0%
Memory size1.8 KiB
2021-04-27T05:58:28.378883image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile63.8
Q14389
median41338
Q3277573
95-th percentile3007394
Maximum125762412
Range125762410
Interquartile range (IQR)273184

Descriptive statistics

Standard deviation12311357.24
Coefficient of variation (CV)6.952294673
Kurtosis96.87742669
Mean1770833.634
Median Absolute Deviation (MAD)41276
Skewness9.768592517
Sum377187564
Variance1.515695171 × 1014
MonotocityNot monotonic
2021-04-27T05:58:28.515138image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1257624122
 
0.9%
1832
 
0.9%
2196471
 
0.5%
190741
 
0.5%
2223581
 
0.5%
390611
 
0.5%
6898121
 
0.5%
82081
 
0.5%
6574521
 
0.5%
6611
 
0.5%
Other values (201)201
94.4%
ValueCountFrequency (%)
21
0.5%
81
0.5%
151
0.5%
181
0.5%
241
0.5%
291
0.5%
441
0.5%
481
0.5%
491
0.5%
581
0.5%
ValueCountFrequency (%)
1257624122
0.9%
253818711
0.5%
142966401
0.5%
128091691
0.5%
43880081
0.5%
43632431
0.5%
41938281
0.5%
40736441
0.5%
33822241
0.5%
31776351
0.5%

New Recovered
Real number (ℝ)

HIGH CORRELATION

Distinct125
Distinct (%)58.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9747.699531
Minimum-1
Maximum692115
Zeros0
Zeros (%)0.0%
Memory size1.8 KiB
2021-04-27T05:58:28.647165image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median64
Q31133
95-th percentile14286
Maximum692115
Range692116
Interquartile range (IQR)1134

Descriptive statistics

Standard deviation68552.43311
Coefficient of variation (CV)7.03267811
Kurtosis92.64884973
Mean9747.699531
Median Absolute Deviation (MAD)65
Skewness9.527274462
Sum2076260
Variance4699436086
MonotocityNot monotonic
2021-04-27T05:58:28.766880image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-175
35.2%
263
 
1.4%
5573
 
1.4%
75402
 
0.9%
6921152
 
0.9%
752
 
0.9%
92
 
0.9%
332
 
0.9%
32
 
0.9%
52
 
0.9%
Other values (115)118
55.4%
ValueCountFrequency (%)
-175
35.2%
32
 
0.9%
52
 
0.9%
82
 
0.9%
92
 
0.9%
101
 
0.5%
111
 
0.5%
141
 
0.5%
151
 
0.5%
171
 
0.5%
ValueCountFrequency (%)
6921152
0.9%
2185591
0.5%
512361
0.5%
423971
0.5%
416521
0.5%
408781
0.5%
218901
0.5%
204631
0.5%
167531
0.5%
145471
0.5%

Active Cases
Real number (ℝ≥0)

HIGH CORRELATION

Distinct200
Distinct (%)93.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean266376.9202
Minimum1
Maximum18912864
Zeros0
Zeros (%)0.0%
Memory size1.8 KiB
2021-04-27T05:58:28.896528image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile10
Q1305
median4685
Q328050
95-th percentile297469.4
Maximum18912864
Range18912863
Interquartile range (IQR)27745

Descriptive statistics

Standard deviation1892396.063
Coefficient of variation (CV)7.104204303
Kurtosis89.16795653
Mean266376.9202
Median Absolute Deviation (MAD)4661
Skewness9.322046847
Sum56738284
Variance3.581162858 × 1012
MonotocityNot monotonic
2021-04-27T05:58:29.016393image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16
 
2.8%
333
 
1.4%
122
 
0.9%
432
 
0.9%
22
 
0.9%
189128642
 
0.9%
19382
 
0.9%
102
 
0.9%
886981
 
0.5%
170581
 
0.5%
Other values (190)190
89.2%
ValueCountFrequency (%)
16
2.8%
22
 
0.9%
31
 
0.5%
61
 
0.5%
102
 
0.9%
122
 
0.9%
131
 
0.5%
201
 
0.5%
241
 
0.5%
291
 
0.5%
ValueCountFrequency (%)
189128642
0.9%
68581541
0.5%
28145441
0.5%
11406931
0.5%
10319431
0.5%
5179671
0.5%
4612081
0.5%
4491131
0.5%
4104201
0.5%
3220641
0.5%

Serious/Critical
Real number (ℝ)

HIGH CORRELATION

Distinct107
Distinct (%)50.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1555.910798
Minimum-1
Maximum110492
Zeros0
Zeros (%)0.0%
Memory size1.8 KiB
2021-04-27T05:58:29.152033image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median18
Q3238
95-th percentile4809.2
Maximum110492
Range110493
Interquartile range (IQR)239

Descriptive statistics

Standard deviation10732.05518
Coefficient of variation (CV)6.897603124
Kurtosis99.98623872
Mean1555.910798
Median Absolute Deviation (MAD)19
Skewness9.964081067
Sum331409
Variance115177008.4
MonotocityNot monotonic
2021-04-27T05:58:29.268695image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-163
29.6%
110
 
4.7%
245
 
2.3%
45
 
2.3%
54
 
1.9%
234
 
1.9%
24
 
1.9%
143
 
1.4%
33
 
1.4%
73
 
1.4%
Other values (97)109
51.2%
ValueCountFrequency (%)
-163
29.6%
110
 
4.7%
24
 
1.9%
33
 
1.4%
45
 
2.3%
54
 
1.9%
62
 
0.9%
73
 
1.4%
82
 
0.9%
91
 
0.5%
ValueCountFrequency (%)
1104922
0.9%
99131
0.5%
89441
0.5%
83181
0.5%
59781
0.5%
56451
0.5%
52061
0.5%
50491
0.5%
49421
0.5%
48261
0.5%

Total Cases/1M
Real number (ℝ≥0)

Distinct212
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31622.09437
Minimum8
Maximum168808
Zeros0
Zeros (%)0.0%
Memory size1.8 KiB
2021-04-27T05:58:29.389374image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile253.6
Q12179
median17687
Q355980
95-th percentile94163.8
Maximum168808
Range168800
Interquartile range (IQR)53801

Descriptive statistics

Standard deviation35248.45058
Coefficient of variation (CV)1.114677927
Kurtosis1.456697812
Mean31622.09437
Median Absolute Deviation (MAD)16755
Skewness1.284098878
Sum6735506.1
Variance1242453268
MonotocityNot monotonic
2021-04-27T05:58:29.503070image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
292
 
0.9%
673021
 
0.5%
7831
 
0.5%
656191
 
0.5%
1509881
 
0.5%
10051
 
0.5%
9471
 
0.5%
14171
 
0.5%
43281
 
0.5%
742571
 
0.5%
Other values (202)202
94.8%
ValueCountFrequency (%)
81
0.5%
151
0.5%
161
0.5%
292
0.9%
441
0.5%
461
0.5%
751
0.5%
1011
0.5%
2021
0.5%
2081
0.5%
ValueCountFrequency (%)
1688081
0.5%
1538851
0.5%
1509881
0.5%
1487251
0.5%
1271561
0.5%
1140011
0.5%
1045571
0.5%
986991
0.5%
983601
0.5%
979591
0.5%

Deaths/1M
Real number (ℝ)

Distinct173
Distinct (%)81.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean521.5488263
Minimum-1
Maximum2791
Zeros0
Zeros (%)0.0%
Memory size1.8 KiB
2021-04-27T05:58:29.625742image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile0.36
Q128
median196
Q3820
95-th percentile1994.6
Maximum2791
Range2792
Interquartile range (IQR)792

Descriptive statistics

Standard deviation672.015967
Coefficient of variation (CV)1.288500584
Kurtosis1.631213029
Mean521.5488263
Median Absolute Deviation (MAD)189
Skewness1.533219862
Sum111089.9
Variance451605.46
MonotocityNot monotonic
2021-04-27T05:58:29.730924image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-110
 
4.7%
106
 
2.8%
24
 
1.9%
1573
 
1.4%
83
 
1.4%
303
 
1.4%
73
 
1.4%
253
 
1.4%
332
 
0.9%
13542
 
0.9%
Other values (163)174
81.7%
ValueCountFrequency (%)
-110
4.7%
0.31
 
0.5%
0.41
 
0.5%
0.52
 
0.9%
11
 
0.5%
24
 
1.9%
31
 
0.5%
41
 
0.5%
52
 
0.9%
73
 
1.4%
ValueCountFrequency (%)
27911
0.5%
27621
0.5%
27021
0.5%
26181
0.5%
25131
0.5%
23321
0.5%
23041
0.5%
22451
0.5%
21051
0.5%
20631
0.5%

Total Tests
Real number (ℝ)

Distinct206
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9253218.493
Minimum-1
Maximum439668878
Zeros0
Zeros (%)0.0%
Memory size1.8 KiB
2021-04-27T05:58:29.852602image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile6448.4
Q1109233
median858830
Q34385992
95-th percentile38414040.8
Maximum439668878
Range439668879
Interquartile range (IQR)4276759

Descriptive statistics

Standard deviation38762965.63
Coefficient of variation (CV)4.18913329
Kurtosis83.26391531
Mean9253218.493
Median Absolute Deviation (MAD)831242
Skewness8.516869959
Sum1970935539
Variance1.502567504 × 1015
MonotocityNot monotonic
2021-04-27T05:58:29.967330image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-18
 
3.8%
9632251
 
0.5%
352790241
 
0.5%
2336471
 
0.5%
52806001
 
0.5%
81107601
 
0.5%
12223101
 
0.5%
9683551
 
0.5%
122748471
 
0.5%
1500883501
 
0.5%
Other values (196)196
92.0%
ValueCountFrequency (%)
-18
3.8%
44181
 
0.5%
45001
 
0.5%
53181
 
0.5%
72021
 
0.5%
100251
 
0.5%
123591
 
0.5%
125971
 
0.5%
145321
 
0.5%
164931
 
0.5%
ValueCountFrequency (%)
4396688781
0.5%
2779188101
0.5%
1500883501
0.5%
1278000001
0.5%
745050351
0.5%
571260171
0.5%
540613321
0.5%
458842581
0.5%
443742231
0.5%
435381041
0.5%

Tests/1M
Real number (ℝ)

Distinct206
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean589897.3521
Minimum-1
Maximum6844699
Zeros0
Zeros (%)0.0%
Memory size1.8 KiB
2021-04-27T05:58:30.088959image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile4676.4
Q146234
median217553
Q3683342
95-th percentile2377401
Maximum6844699
Range6844700
Interquartile range (IQR)637108

Descriptive statistics

Standard deviation997252.144
Coefficient of variation (CV)1.690551993
Kurtosis14.45466354
Mean589897.3521
Median Absolute Deviation (MAD)206377
Skewness3.450399117
Sum125648136
Variance9.945118388 × 1011
MonotocityNot monotonic
2021-04-27T05:58:30.448039image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-18
 
3.8%
462341
 
0.5%
5956351
 
0.5%
7722731
 
0.5%
3122761
 
0.5%
15284941
 
0.5%
58031
 
0.5%
257661
 
0.5%
26646131
 
0.5%
11393641
 
0.5%
Other values (196)196
92.0%
ValueCountFrequency (%)
-18
3.8%
7551
 
0.5%
16071
 
0.5%
39091
 
0.5%
51881
 
0.5%
52141
 
0.5%
52181
 
0.5%
58031
 
0.5%
62481
 
0.5%
64231
 
0.5%
ValueCountFrequency (%)
68446991
0.5%
60732721
0.5%
51178621
0.5%
43168671
0.5%
41092381
0.5%
38529291
0.5%
37630981
0.5%
33915941
0.5%
26646131
0.5%
25940961
0.5%

Population
Real number (ℝ)

Distinct212
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29912676.84
Minimum-1
Maximum1391086597
Zeros0
Zeros (%)0.0%
Memory size1.8 KiB
2021-04-27T05:58:30.568712image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile33866.6
Q1778545
median6688883
Q324914928
95-th percentile113372996.8
Maximum1391086597
Range1391086598
Interquartile range (IQR)24136383

Descriptive statistics

Standard deviation104383457.2
Coefficient of variation (CV)3.489606023
Kurtosis137.9470394
Mean29912676.84
Median Absolute Deviation (MAD)6466411
Skewness10.84448423
Sum6371400166
Variance1.089590614 × 1016
MonotocityNot monotonic
2021-04-27T05:58:30.685367image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-12
 
0.9%
1660295111
 
0.5%
336944011
 
0.5%
372676471
 
0.5%
303961
 
0.5%
17494121
 
0.5%
1107708721
 
0.5%
69475071
 
0.5%
653917941
 
0.5%
99021
 
0.5%
Other values (202)202
94.8%
ValueCountFrequency (%)
-12
0.9%
8031
0.5%
35621
0.5%
57711
0.5%
99021
0.5%
110771
0.5%
151121
0.5%
264221
0.5%
303961
0.5%
336831
0.5%
ValueCountFrequency (%)
13910865971
0.5%
3325902771
0.5%
2758854751
0.5%
2243830231
0.5%
2137947781
0.5%
2102887571
0.5%
1660295111
0.5%
1459857371
0.5%
1300389531
0.5%
1261588661
0.5%

Continent
Categorical

Distinct7
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size13.6 KiB
Africa
57 
Europe
48 
Asia
47 
North America
36 
South America
14 
Other values (2)
11 

Length

Max length17
Median length6
Mean length7.638497653
Min length3

Characters and Unicode

Total characters1627
Distinct characters22
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAll
2nd rowNorth America
3rd rowAsia
4th rowSouth America
5th rowEurope
ValueCountFrequency (%)
Africa57
26.8%
Europe48
22.5%
Asia47
22.1%
North America36
16.9%
South America14
 
6.6%
Australia/Oceania9
 
4.2%
All2
 
0.9%
2021-04-27T05:58:30.917746image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-04-27T05:58:30.988589image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
africa57
21.7%
america50
19.0%
europe48
18.3%
asia47
17.9%
north36
13.7%
south14
 
5.3%
australia/oceania9
 
3.4%
all2
 
0.8%

Most occurring characters

ValueCountFrequency (%)
r200
12.3%
a190
11.7%
i172
10.6%
A165
10.1%
c116
 
7.1%
e107
 
6.6%
o98
 
6.0%
u71
 
4.4%
t59
 
3.6%
f57
 
3.5%
Other values (12)392
24.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1296
79.7%
Uppercase Letter272
 
16.7%
Space Separator50
 
3.1%
Other Punctuation9
 
0.6%

Most frequent character per category

ValueCountFrequency (%)
r200
15.4%
a190
14.7%
i172
13.3%
c116
9.0%
e107
8.3%
o98
7.6%
u71
 
5.5%
t59
 
4.6%
f57
 
4.4%
s56
 
4.3%
Other values (5)170
13.1%
ValueCountFrequency (%)
A165
60.7%
E48
 
17.6%
N36
 
13.2%
S14
 
5.1%
O9
 
3.3%
ValueCountFrequency (%)
50
100.0%
ValueCountFrequency (%)
/9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1568
96.4%
Common59
 
3.6%

Most frequent character per script

ValueCountFrequency (%)
r200
12.8%
a190
12.1%
i172
11.0%
A165
10.5%
c116
 
7.4%
e107
 
6.8%
o98
 
6.2%
u71
 
4.5%
t59
 
3.8%
f57
 
3.6%
Other values (10)333
21.2%
ValueCountFrequency (%)
50
84.7%
/9
 
15.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII1627
100.0%

Most frequent character per block

ValueCountFrequency (%)
r200
12.3%
a190
11.7%
i172
10.6%
A165
10.1%
c116
 
7.1%
e107
 
6.6%
o98
 
6.0%
u71
 
4.4%
t59
 
3.6%
f57
 
3.5%
Other values (12)392
24.1%
Distinct212
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3119739402
Minimum-33.33333333
Maximum23.52941176
Zeros0
Zeros (%)0.0%
Memory size1.8 KiB
2021-04-27T05:58:31.125196image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-33.33333333
5-th percentile-0.475243863
Q1-0.0001065708382
median0.2271283996
Q30.5036839237
95-th percentile1.503103045
Maximum23.52941176
Range56.8627451
Interquartile range (IQR)0.5037904946

Descriptive statistics

Standard deviation3.330447
Coefficient of variation (CV)10.67540128
Kurtosis64.83943218
Mean0.3119739402
Median Absolute Deviation (MAD)0.2297422318
Skewness-2.511289499
Sum66.45044926
Variance11.09187722
MonotocityNot monotonic
2021-04-27T05:58:31.232935image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.50368392372
 
0.9%
-0.082918739641
 
0.5%
0.45687063141
 
0.5%
4.6916183451
 
0.5%
0.57501436471
 
0.5%
0.53200131651
 
0.5%
-0.001515197431
 
0.5%
0.4047154391
 
0.5%
0.51842251431
 
0.5%
-0.44843049331
 
0.5%
Other values (202)202
94.8%
ValueCountFrequency (%)
-33.333333331
0.5%
-101
0.5%
-51
0.5%
-41
0.5%
-3.7037037041
0.5%
-2.0408163271
0.5%
-1.5873015871
0.5%
-0.80645161291
0.5%
-0.62893081761
0.5%
-0.57803468211
0.5%
ValueCountFrequency (%)
23.529411761
0.5%
19.444444441
0.5%
6.1754385961
0.5%
4.6916183451
0.5%
4.3959610531
0.5%
4.3956043961
0.5%
4.14657351
0.5%
2.0485661291
0.5%
1.9532864821
0.5%
1.8737818831
0.5%
Distinct187
Distinct (%)87.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6753253852
Minimum-100
Maximum100
Zeros0
Zeros (%)0.0%
Memory size1.8 KiB
2021-04-27T05:58:31.353613image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-100
5-th percentile-18
Q1-0.7092198582
median0.1045276998
Q30.5632216277
95-th percentile6.21978022
Maximum100
Range200
Interquartile range (IQR)1.272441486

Descriptive statistics

Standard deviation28.20249797
Coefficient of variation (CV)41.76134734
Kurtosis9.274029719
Mean0.6753253852
Median Absolute Deviation (MAD)0.5589360384
Skewness0.704310305
Sum143.844307
Variance795.380892
MonotocityNot monotonic
2021-04-27T05:58:31.469303image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10010
 
4.7%
-1006
 
2.8%
-8.3333333332
 
0.9%
-2.8571428572
 
0.9%
-0.70921985822
 
0.9%
-1.1764705882
 
0.9%
-1.0204081632
 
0.9%
-3.4482758622
 
0.9%
-0.59171597632
 
0.9%
-502
 
0.9%
Other values (177)181
85.0%
ValueCountFrequency (%)
-1006
2.8%
-502
 
0.9%
-33.333333332
 
0.9%
-201
 
0.5%
-16.666666671
 
0.5%
-102
 
0.9%
-8.3333333332
 
0.9%
-6.6666666671
 
0.5%
-6.251
 
0.5%
-5.8823529411
 
0.5%
ValueCountFrequency (%)
10010
4.7%
7.8571428571
 
0.5%
5.1282051281
 
0.5%
4.0540540541
 
0.5%
3.1251
 
0.5%
2.9411764711
 
0.5%
2.3364485981
 
0.5%
1.848667241
 
0.5%
1.770359131
 
0.5%
1.760563381
 
0.5%
Distinct211
Distinct (%)99.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.03298882844
Minimum-50
Maximum5.588235294
Zeros0
Zeros (%)0.0%
Memory size1.8 KiB
2021-04-27T05:58:31.593708image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum-50
5-th percentile-1.568238213
Q1-0.009756097561
median0.1848006249
Q30.5565654105
95-th percentile1.687218904
Maximum5.588235294
Range55.58823529
Interquartile range (IQR)0.5663215081

Descriptive statistics

Standard deviation3.724675586
Coefficient of variation (CV)-112.9071799
Kurtosis154.8583089
Mean-0.03298882844
Median Absolute Deviation (MAD)0.2277928863
Skewness-11.68589196
Sum-7.026620459
Variance13.87320822
MonotocityNot monotonic
2021-04-27T05:58:31.698465image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.54644808742
 
0.9%
0.55033534192
 
0.9%
-0.001608156571
 
0.5%
-0.0021739130431
 
0.5%
0.11837060291
 
0.5%
-0.023906287351
 
0.5%
0.05495658431
 
0.5%
0.0037898885771
 
0.5%
-0.0015526744821
 
0.5%
0.89325144441
 
0.5%
Other values (201)201
94.4%
ValueCountFrequency (%)
-501
0.5%
-12.51
0.5%
-6.6666666671
0.5%
-5.5555555561
0.5%
-4.1666666671
0.5%
-3.4482758621
0.5%
-2.2727272731
0.5%
-2.0833333331
0.5%
-2.0408163271
0.5%
-1.7241379311
0.5%
ValueCountFrequency (%)
5.5882352941
0.5%
4.2718950851
0.5%
3.911980441
0.5%
2.9612756261
0.5%
2.8427673751
0.5%
2.8423294141
0.5%
2.3566378631
0.5%
2.2645606171
0.5%
2.2035906861
0.5%
1.825372681
0.5%

Interactions

2021-04-27T05:58:00.130665image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-27T05:58:00.250372image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-27T05:58:00.365072image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-27T05:58:00.477738image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-27T05:58:00.593459image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-27T05:58:00.707155image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-27T05:58:00.822845image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-27T05:58:01.004361image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-27T05:58:01.118058image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-27T05:58:01.224773image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-27T05:58:01.334479image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-27T05:58:01.461144image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-27T05:58:01.599770image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-27T05:58:01.716454image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-27T05:58:01.839097image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-27T05:58:01.949833image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-27T05:58:02.064494image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-27T05:58:02.176230image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-27T05:58:02.278952image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-27T05:58:02.388665image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-27T05:58:02.499363image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-27T05:58:02.614058image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-27T05:58:02.717777image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-27T05:58:02.830480image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-27T05:58:02.932207image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-27T05:58:03.030943image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-27T05:58:03.139652image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-27T05:58:03.253350image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-27T05:58:03.356067image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-27T05:58:03.469769image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-27T05:58:03.574489image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-27T05:58:03.684197image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-27T05:58:03.790911image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-27T05:58:03.891643image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
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2021-04-27T05:58:25.914722image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-27T05:58:26.015453image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-27T05:58:26.127154image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-27T05:58:26.240850image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-04-27T05:58:26.344573image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Correlations

2021-04-27T05:58:31.812162image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-04-27T05:58:32.055508image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-04-27T05:58:32.300833image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-04-27T05:58:32.550154image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-04-27T05:58:26.553014image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
A simple visualization of nullity by column.
2021-04-27T05:58:26.875201image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

CountryTotal CasesNew CasesTotal DeathsNew DeathsTotal RecoveredNew RecoveredActive CasesSerious/CriticalTotal Cases/1MDeaths/1MTotal TestsTests/1MPopulationContinentPercentage Increase in CasesPercentage Increase in DeathPercentage Increase in Recovered
0World147797848744434.0312257210005.0125762412692115.01891286411049218961.0400.6-1-1-1All0.5036840.3204090.550335
1USA3282618242097.0586157273.02538187140878.06858154991398699.01762.04396688781321953332590277North America0.1282420.0465750.161052
2India17306300354531.01951162806.014296640218559.02814544894412441.0140.02779188101997851391086597Asia2.0485661.4381191.528744
3Brazil1434078732572.03909251316.01280916942397.01140693831867077.01829.043538104203644213794778South America0.2271280.3366370.330989
4France549804424465.0102858145.0436324341652.01031943597884079.01573.074505035113936465391794Europe0.4449760.1409710.954611
5Russia47625698780.0108232332.043880087540.0266329230032624.0741.0127800000875428145985737Europe0.1843540.3067480.171832
6Turkey462996938553.038358347.0407364451236.0517967359054419.0451.04588425853930785080099Asia0.8326840.9046351.257744
7UK44048821712.012742811.041938284674.08362624364609.01869.0150088350220142768177756Europe0.0388660.0086320.111449
8Italy396267013154.0119238217.0338222413176.0461208286265619.01975.05712601794596860388934Europe0.3319480.1819890.389566
9Spain34819697949.07768949.031776357537.0226645229774449.01661.04437422394878346769617Europe0.2282900.0630720.237189

Last rows

CountryTotal CasesNew CasesTotal DeathsNew DeathsTotal RecoveredNew RecoveredActive CasesSerious/CriticalTotal Cases/1MDeaths/1MTotal TestsTests/1MPopulationContinentPercentage Increase in CasesPercentage Increase in DeathPercentage Increase in Recovered
203Fiji914.02-1.065-1.024-1101.02.04743052597901756Australia/Oceania4.395604-50.000000-1.538462
204Anguilla7214.0-1-1.029-1.043-14764.0-1.017376114981515112North America19.444444100.000000-3.448276
205Falkland Islands63-1.0-1-1.062-1.01-117687.0-1.0720220218983562South America-1.587302100.000000-1.612903
206Macao49-1.0-1-1.048-1.01-175.0-1.044186729656555Asia-2.040816100.000000-2.083333
207Vatican City27-1.0-1-1.015-1.012-133624.0-1.0-1-1803Europe-3.703704100.000000-6.666667
208Saint Pierre Miquelon25-1.0-1-1.024-1.01-14332.0-1.053189215045771North America-4.000000100.000000-4.166667
209Solomon Islands20-1.0-1-1.018-1.02-129.0-1.045006423700565Australia/Oceania-5.000000100.000000-5.555556
210Western Sahara10-1.01-1.08-1.01-116.02.0-1-1609267Africa-10.000000-100.000000-12.500000
211Samoa3-1.0-1-1.02-1.01-115.0-1.0-1-1199492Australia/Oceania-33.333333100.000000-50.000000
212Total:147797848744434.0312257210005.0125762412692115.01891286411049218961.1400.6-1-1-1All0.5036840.3204090.550335